AI Infrastructure Ecosystem of 2022
With hundreds of AI/ML infrastructure tools on the market, how do you make sense of it all?
Which parts of the stack are mature? How do you know where to invest your precious time and resources to get the best results for your rapidly growing team of data engineers and data scientists?
Our first annual AI Infrastructure Ecosystem report answers these questions and more. It gives team leads, technical executives and architects the keys they need to build or expand your infrastructure by providing a comprehensive and clear overview of the entire AI/ML infrastructure landscape.
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Intelligence is the great competitive advantage in history. It’s not just whether we have it, but whether we can wield it effectively. But over the last decade, intelligence has undergone a profound change. It’s no longer just confined to our heads because the rise of artificial intelligence and machine learning (AI/ML) techniques now makes it possible to productionize intelligence to solve previously unsolvable challenges. We can use AI to spot problems early on in our production lines, predict customer churn, reclaim budget, streamline support requests, translate languages, highlight key passages in legal documents, detect fraud, iterate on new design ideas and much, much more.
Yet this production intelligence has largely remained the province of highly technical teams and big tech companies. Often these teams built their own AI/ML infrastructure from scratch because there was nothing on the market to support their efforts. Yet over the last five years we have seen a rapid proliferation of new tools and platforms that allow enterprises and small to medium businesses to benefit from the intelligence revolution. However, building the right AI/ML infrastructure that fits specific company needs is still a significant barrier.
Only 26% of teams we surveyed were very satisfied with their current AI/ML infrastructure. 55% were only somewhat satisfied, while 17% were somewhat unsatisfied, and 3% were very unsatisfied. In other words, most teams see a lot of room for improvement.
That is because the AI/ML infrastructure landscape is vast, complex and rapidly evolving. It’s difficult to understand the capabilities of each platform and to see where they fit into existing systems without a lot of research and time invested. AI/ML systems are also complex because there is no one tool that does everything perfectly, so building a modern AI/ML stack involves many different tools and components. Even worse, marketing teams often obscure the capabilities of systems or promise that a platform can do everything equally well, when the reality is usually very different. Finally, building a robust infrastructure requires buy-in from many different stakeholders across an organization, everyone from data scientists, to data engineers, to IT infrastructure architects, to support teams, to network and security engineers.
Up to this point, much of what has been written on AI/ML focuses on building excitement around AI, the state of ML adoption, or on outlining the state of AI/ML research. While it is nice to read about the incredible possibilities from utilizing AI/ML in your company, what is truly needed is thorough coverage of the infrastructure available and possible directions your company can take to achieve your business goals. Here we focus on how to sustainably build your AI/ML infrastructure to set your company up for success over the long term, as your capabilities, needs and demands evolve.
AI/ML teams are growing and this report aims to give every company the keys to build their AI/ML infrastructure by providing a comprehensive and clear overview of the AI/ML infrastructure landscape.
We provide insights about realistic capabilities and tradeoffs for many different platforms, as well as projections about how infrastructure requirements may evolve over the next five years from AI/ML experts.
The good news is that the majority of companies we surveyed found that the benefits they got from their AI/ML infrastructured outweighed the costs in two years or less. That means if you invest in the right infrastructure, after surveying the field and considering it carefully, you can reap rewards swiftly.
By aligning your business goals with your wisely built AI/ML infrastructure, you can push the boundaries of what is possible for your company now and in the future.
We’ve discovered that the teams surveyed often faced their biggest challenges with collecting the data they need along with cleaning, QAing and transforming that data, which falls squarely on the shoulders of data engineers.
That’s reflected in the composition of teams as well. Most companies surveyed employed more data engineers than data scientists. It’s also reflected in where teams are spending the most time and money.
Only 26% of teams we surveyed were very satisfied with their current AI/ML infrastructure. 55% percent were only somewhat satisfied, while 17% were somewhat unsatisfied, and 3% were very unsatisfied. In other words, most teams see a lot of room for improvement.
This is the information we needed to make much better decisions about what's out there and what's ready for prime time in machine learning infrastructure.
Fortune 500 Chief of Artificial Intelligence
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